CNN Architectures for Large-Scale Audio Classification
Abstract
Convolutional Neural Networks (CNNs) have proven very effective
in image classification and have shown promise for audio classification.
We apply various CNN architectures to audio and investigate
their ability to classify videos with a very large scale data set of 70M
training videos (5.24 million hours) with 30,871 labels. We examine
fully connected Deep Neural Networks (DNNs), AlexNet [1],
VGG [2], Inception [3], and ResNet [4]. We explore the effects of
training with different sized subsets of the 70M training videos. Additionally
we report the effect of training over different subsets of
the 30,871 labels. While our dataset contains video-level labels, we
are also interested in Acoustic Event Detection (AED) and train a
classifier on embeddings learned from the video-level task on AudioSet
[5]. We find that derivatives of image classification networks
do well on our audio classification task, that increasing the number
of labels we train on provides some improved performance over subsets
of labels, that performance of models improves as we increase
training set size, and that a model using embeddings learned from
the video-level task do much better than a baseline on the AudioSet
classification task.
in image classification and have shown promise for audio classification.
We apply various CNN architectures to audio and investigate
their ability to classify videos with a very large scale data set of 70M
training videos (5.24 million hours) with 30,871 labels. We examine
fully connected Deep Neural Networks (DNNs), AlexNet [1],
VGG [2], Inception [3], and ResNet [4]. We explore the effects of
training with different sized subsets of the 70M training videos. Additionally
we report the effect of training over different subsets of
the 30,871 labels. While our dataset contains video-level labels, we
are also interested in Acoustic Event Detection (AED) and train a
classifier on embeddings learned from the video-level task on AudioSet
[5]. We find that derivatives of image classification networks
do well on our audio classification task, that increasing the number
of labels we train on provides some improved performance over subsets
of labels, that performance of models improves as we increase
training set size, and that a model using embeddings learned from
the video-level task do much better than a baseline on the AudioSet
classification task.
Research Areas
